Social networks (SN) emerge from interactions among a large number of heterogeneous entities, which behave according to patterns that vary over multiple time scales and over multiple types of relationships. For instance, consider a scientific community that interacts within research groups. Social networks may be used to represent co-authorship among researchers, collaboration between academic departments, or interactions among research groups, over different periods of time. Studying these networks may lead to a solid understanding of the evolution of a research area. Expected results range from assessing the importance and impact of certain authors or groups of authors, to disseminating new theories or experimental practices [75, 89]. Another example is society in general, since it represents one of the most complex systems, with millions of citizens interacting at different rates, and following different patterns. Ultimately, these interactions lead to a complex social network that plays a key role in several aspects of contemporary life, such as propagation of new ideas, wealth generation, and disease transmission.
In the past decades, social scientists investigated the interactions between individuals and institutions, aiming at determining some collective social behavior . However, the objects of study (typically friendship, collaboration, and communication relationships) are hard to observe experimentally over time for a large number of people. Such experiments are restricted to observations during short time windows, and for small populations. Moreover, since the observation process is highly subjective, conclusions are uncertain.
As the number of daily activities that modern society performs online increases, the process of collecting data on personal communication and interaction becomes viable. It becomes possible to know more about what people learn, how they work, and even how they spend their free time. Online activities generate traces of the interactions between people in the form of information available on the Web. These traces, captured by logs, describe interactions in various Web applications such as search engines, online social networking services, and e-commerce applications [31, 75, 114].
The study of social networks based on these logs leads to practical and theoretical information  on the behavior and evolution of Web-based networks . With this goal in mind, the SN research line will address fundamental issues related to the three previously mentioned great challenges. With respect to Challenge 1, characterizing and modeling topological and functional properties of several social networks have a key role in the modeling of collective social user behavior. Moreover, knowledge about these properties and about the interaction between social networks and the underlying technological networks may be exploited to build new techniques and algorithms to meet Challenge 2, namely, treatment of information. Research on social networks has also had an impact on efficiency, reliability, and security in large scale distributed systems, such as the Web [17, 94, 121]. In this perspective, the study of social networks also contributes to the development of algorithms and protocols to address Challenge 3. Therefore, research efforts on social networks influence all three layers of our unified view of the Web, and contribute to all three great challenges addressed by INWeb.
The Social Network research line will be carried out, primarily, by the researchers Virgílio Almeida (UFMG), Jussara Almeida (UFMG), and Cristina Murta (CEFET-MG).